Refine
Departments, institutes and facilities
- Fachbereich Informatik (64)
- Fachbereich Angewandte Naturwissenschaften (49)
- Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) (42)
- Fachbereich Ingenieurwissenschaften und Kommunikation (33)
- Fachbereich Wirtschaftswissenschaften (22)
- Institut für Cyber Security & Privacy (ICSP) (18)
- Institut für funktionale Gen-Analytik (IFGA) (17)
- Institut für Verbraucherinformatik (IVI) (16)
- Institute of Visual Computing (IVC) (16)
- Institut für Sicherheitsforschung (ISF) (8)
Document Type
- Conference Object (96)
- Article (88)
- Preprint (9)
- Doctoral Thesis (6)
- Part of a Book (5)
- Report (4)
- Book (monograph, edited volume) (3)
- Master's Thesis (3)
- Conference Proceedings (1)
- Research Data (1)
Year of publication
- 2019 (217) (remove)
Language
- English (217) (remove)
Keywords
- lignin (4)
- Navigation (3)
- security (3)
- work engagement (3)
- Aminoacylase (2)
- Design (2)
- Drosophila (2)
- Exergame (2)
- Extrusion blow molding (2)
- FPGA (2)
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.